Thrombosis and hemostasis testing involves the in vitro study of the ability of blood to form clots and to dissolve clots in vivo. A variety of coagulation (hemostasis) assays are used to identify congenital or acquired disorders of the coagulation system and to monitor the administration of therapeutic drugs.
Two assays, the, prothrombin time (PT) and activated partial thromboplastin time (APTT), are widely used to screen for abnormalities in the coagulation system, although several other screening assays can be used, e.g. protein C, fibrinogen, protein S and/or thrombin time. These assays usually measure clot time, the time required to initiate clot formation following the addition of a coagulation activating agent to blood or plasma. (Some variations of the PT also use the amplitude of the change in optical signal to estimate fibrinogen concentration). Automated methods determine clot time based on changes in electromechanical properties, clot elasticity, light scattering, fibrin adhesion, impedance or other properties. For light scattering methods, data is gathered that represents the transmission of light through the specimen as a function of time (one example of an optical time-dependent measurement profile).
Blood coagulation is affected by administration of drugs, in addition to the vast array of internal factors and proteins that normally influence clot formation. For example, heparin is a widely-used therapeutic drug that is used to prevent thrombosis following surgery or under other conditions, or is used to combat existing thrombosis. The administration of heparin is typically monitored using the APTT assay, which gives a prolonged clot time in the presence of heparin. Clot times for PT assays are affected to a much smaller degree. Since a number of plasma abnormalities or therapeutic conditions may cause a prolonged APTT result, one or several additional tests are needed to isolate the exact source of the abnormality. The ability to discriminate between these effectors from screening assay results may be clinically significant.
The present invention was conceived of and developed for presenting the relationships between an unknown sample and samples from known populations. The invention is intended to facilitate analysis of information embedded in the data from coagulation assays that is not included in the conventional data analysis for these assays. The additional information can help discriminate between underlying conditions and aid in the identification of otherwise undetected conditions.
The present invention is directed to a method for presenting the relationship between data from an assay relating to thrombosis-hemostasis on an unknown sample, and data from a plurality of assays relating to thrombosis-hemostasis from known sample populations, including:
(a) providing data from at least one time-dependent measurement profile for each of a plurality of known blood samples (the blood samples can be whole blood, or a portion thereof such as plasma);
(b) measuring a property over time to derive at least one time-dependent measurement for an unknown blood sample;
(c) transforming data from steps (a) and (b) to a plurality of predictor variables which sufficiently capture the information content of the time-dependent measurements from both the known blood samples and unknown blood sample;
(d) presenting the data from said unknown blood sample time-dependent measurement profile relative to the data from said known blood sample time-dependent measurement profiles using the presentation method of either steps (e), (f), and (g), or steps (h) and (i);
(e) creating a topological feature map of the sets of predictor variables from step (c) of the known samples in step (a) whose spatial locations within the map correspond to intrinsic features of the sets of predictor variables;
(f) determining the position on the map of the unknown sample corresponding to its set of predictor variables;
(g) presenting the data from said unknown blood sample time-dependent measurement profile relative to the data from said known blood sample time-dependent measurement profiles;
(h) computing the standard deviation for each predictor variable in step (c) of the known samples in step (a);
(i) determining the z-score of the unknown sample in (b) for each predictor variable, and determining if one or more of the z-scores for the unknown sample is greater than a predetermined limit, signifying that the unknown sample is different from the known population represented by the model.